Realtime bandwidth-based communication for assistant systems

ABSTRACT

In one embodiment, a method includes initiating a communication session with a second client system associated with a second user via a communication network, wherein the communication session is initiated in a first modality, receiving a ping to the first client system from the communication network to evaluate available bandwidth on the communication network, estimating, by the first client system, an amount of bandwidth available on the communication network for use by the first client system, determining, by the first client system, the amount of bandwidth available on the communication network for use by the first client system is insufficient for the first modality, and switching the communication session with the second client system to a second modality by the first client system, wherein the second modality uses less bandwidth than the first modality.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patentapplication Ser. No. 16/222,923, filed 17 Dec. 2018, which claims thebenefit, under 35 U.S.C. § 119(e), of U.S. Provisional PatentApplication No. 62/660,876, filed 20 Apr. 2018, which is incorporatedherein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video, motion) in stateful and multi-turn conversations to getassistance. The assistant system may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system may analyze theuser input using natural-language understanding. The analysis may bebased on the user profile for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may use dialogmanagement techniques to manage and forward the conversation flow withthe user. In particular embodiments, the assistant system may furtherassist the user to effectively and efficiently digest the obtainedinformation by summarizing the information. The assistant system mayalso assist the user to be more engaging with an online social networkby providing tools that help the user interact with the online socialnetwork (e.g., creating posts, comments, messages). The assistant systemmay additionally assist the user to manage different tasks such askeeping track of events. In particular embodiments, the assistant systemmay proactively execute tasks that are relevant to user interests andpreferences based on the user profile without a user input. Inparticular embodiments, the assistant system may check privacy settingsto ensure that accessing a user's profile or other user information andexecuting different tasks are permitted subject to the user's privacysettings.

In particular embodiments, the content of Real-Time Communication (RTC)sessions such as Voice-over-Internet calls may be converted to contentin a communication modality having a more compact format that uses lessbandwidth. Voice calls may be converted to text using audio speechrecognition, for example. The compact format may be sent via acommunication network to a recipient device, which may convert thecontent in the compact modality back to the initial modality forpresentation to a recipient user. For example, the recipient device mayreceive the text and convert it to audio using a technique such astext-to-speech. The resulting audio may then be played on a speaker ofthe recipient device. In this way, a reproduction of the content in theinitial modality, e.g., speech, may be presented to the recipient usereven if there is insufficient bandwidth to send the content in theinitial modality. There may be differences between the initial andreproduced content, but the meaning of the reproduced content may besimilar to or the same as the initial content. The compact modality maybe any communication modality that can represent human-understandablecommunication using less data, and thus less network bandwidth, than theinitial modality. For example, text may represent words of a naturallanguage using less data than an audio recording of a person speakingthe words, and the audio encoding may in turn use less data than a videorecording of a person speaking the words. The voice or video calls maybe made using, e.g., a messaging application or any other suitablevoice/video communication service. Automated speech recognition andtext-to-speech functionality may be provided by a smart assistantarchitecture or any other suitable system.

In particular embodiments, modality conversions may be to modalitiesthat can represent fewer types of communication and use less bandwidth,or to modalities that can represent more types of communication and usemore bandwidth. For example, in the conversion of audio to text, audiocan directly represent words and sounds, while text can directlyrepresent words but not sounds (though sounds can be representedindirectly as words in text). In this conversion, sounds from the audiothat are not translated to text by automated speech recognition, such asintonations or other sounds, may be discarded, or may be added to thetext as supplemental content, e.g., as a description of the sound. Whenthe text is converted back to audio, the description in the text of thesupplemental content may be used to generate the corresponding sound,which may be added to the speech generated by a text-to-speechtechnique. Video can represent gestures that are not directlyrepresentable in audio (or text). Conversion from video to audio (ortext) may include discarding gesture content or adding supplementalaudio (or text) content that describes the gestures to the generatedaudio (or text). Subsequently, when the audio (or text) is converted tovideo, the supplemental content may be converted back to gestures, e.g.,by generating video animations of the gestures.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system.

FIG. 4 illustrates an example network environment having links ofdifferent bandwidths.

FIG. 5 illustrates an example network environment in which clientsystems convert content between modalities for communication.

FIG. 6 illustrates an example network environment in which clientsystems convert speech to and from text for communication.

FIG. 7 illustrates a server that converts content between modalities.

FIGS. 8 and 9 illustrate example network environments in which clientsystems convert speech to and from text for communication associatedwith an assistant system.

FIG. 10 illustrates example conversions between content of differentcommunication types associated with different modalities.

FIG. 11 illustrates an example method for communication by convertingbetween different modalities.

FIG. 12 illustrates an example social graph.

FIG. 13 illustrates an example view of an embedding space.

FIG. 14 illustrates an example artificial neural network.

FIG. 15 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, virtual reality(VR) headset, augment reality (AR) smart glasses, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. PatentApplication No. 62/655,751, filed 10 Apr. 2018, U.S. Design patentapplication No. 29/631910, filed 3 Jan. 2018, U.S. Design patentapplication No. 29/631747, filed 2 Jan. 2018, U.S. Design patentapplication No. 29/631913, filed 3 Jan. 2018, and U.S. Design patentapplication No. 29/631914, filed 3 Jan. 2018, each of which isincorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts such as, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, motion, etc. The assistant application 136 maycommunicate the user input to the assistant system 140. Based on theuser input, the assistant system 140 may generate responses. Theassistant system 140 may send the generated responses to the assistantapplication 136. The assistant application 136 may then present theresponses to the user at the client system 130. The presented responsesmay be based on different modalities such as audio, text, image, andvideo. As an example and not by way of limitation, the user may verballyask the assistant application 136 about the traffic information (i.e.,via an audio modality). The assistant application 136 may thencommunicate the request to the assistant system 140. The assistantsystem 140 may accordingly generate the result and send it back to theassistant application 136. The assistant application 136 may furtherpresent the result to the user in text.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Assistant Systems

FIG. 2 illustrates an example architecture of the assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video, motion) in stateful and multi-turn conversations toget assistance. The assistant system 140 may create and store a userprofile comprising both personal and contextual information associatedwith the user. In particular embodiments, the assistant system 140 mayanalyze the user input using natural-language understanding. Theanalysis may be based on the user profile for more personalized andcontext-aware understanding. The assistant system 140 may resolveentities associated with the user input based on the analysis. Inparticular embodiments, the assistant system 140 may interact withdifferent agents to obtain information or services that are associatedwith the resolved entities. The assistant system 140 may generate aresponse for the user regarding the information or services by usingnatural-language generation. Through the interaction with the user, theassistant system 140 may use dialog management techniques to manage andforward the conversation flow with the user. In particular embodiments,the assistant system 140 may further assist the user to effectively andefficiently digest the obtained information by summarizing theinformation. The assistant system 140 may also assist the user to bemore engaging with an online social network by providing tools that helpthe user interact with the online social network (e.g., creating posts,comments, messages). The assistant system 140 may additionally assistthe user to manage different tasks such as keeping track of events. Inparticular embodiments, the assistant system 140 may proactively executepre-authorized tasks that are relevant to user interests and preferencesbased on the user profile, at a time relevant for the user, without auser input. In particular embodiments, the assistant system 140 maycheck privacy settings to ensure that accessing a user's profile orother user information and executing different tasks are permittedsubject to the user's privacy settings. More information on assistingusers subject to privacy settings may be found in U.S. PatentApplication No. 62/675,090, filed 22 May 2018, which is incorporated byreference.

In particular embodiments, the assistant system 140 may receive a userinput from the assistant application 136 in the client system 130associated with the user. In particular embodiments, the user input maybe a user generated input that is sent to the assistant system 140 in asingle turn. If the user input is based on a text modality, theassistant system 140 may receive it at a messaging platform 205. If theuser input is based on an audio modality (e.g., the user may speak tothe assistant application 136 or send a video including speech to theassistant application 136), the assistant system 140 may process itusing an audio speech recognition (ASR) module 210 to convert the userinput into text. If the user input is based on an image or videomodality, the assistant system 140 may process it using opticalcharacter recognition techniques within the messaging platform 205 toconvert the user input into text. The output of the messaging platform205 or the ASR module 210 may be received at an assistant xbot 215. Moreinformation on handling user input based on different modalities may befound in U.S. patent application Ser. No. 16/053,600, filed 2 Aug. 2018,which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may be a type of chatbot. The assistant xbot 215 may comprise a programmable service channel,which may be a software code, logic, or routine that functions as apersonal assistant to the user. The assistant xbot 215 may work as theuser's portal to the assistant system 140. The assistant xbot 215 maytherefore be considered as a type of conversational agent. In particularembodiments, the assistant xbot 215 may send the textual user input to anatural-language understanding (NLU) module 220 to interpret the userinput. In particular embodiments, the NLU module 220 may get informationfrom a user context engine 225 and a semantic information aggregator(SIA) 230 to accurately understand the user input. The user contextengine 225 may store the user profile of the user. The user profile ofthe user may comprise user-profile data including demographicinformation, social information, and contextual information associatedwith the user. The user-profile data may also include user interests andpreferences on a plurality of topics, aggregated through conversationson news feed, search logs, messaging platform 205, etc. The usage of auser profile may be protected behind a privacy check module 245 toensure that a user's information can be used only for his/her benefit,and not shared with anyone else. More information on user profiles maybe found in U.S. patent application Ser. No. 15/967,239, filed 30 Apr.2018, which is incorporated by reference. The semantic informationaggregator 230 may provide ontology data associated with a plurality ofpredefined domains, intents, and slots to the NLU module 220. Inparticular embodiments, a domain may denote a social context ofinteraction, e.g., education. An intent may be an element in apre-defined taxonomy of semantic intentions, which may indicate apurpose of a user interacting with the assistant system 140. Inparticular embodiments, an intent may be an output of the NLU module 220if the user input comprises a text/speech input. The NLU module 220 mayclassify the text/speech input into a member of the pre-definedtaxonomy, e.g., for the input “Play Beethoven's 5th,” the NLU module 220may classify the input as having the intent [IN:play_music]. Inparticular embodiments, a domain may be conceptually a namespace for aset of intents, e.g., music. A slot may be a named sub-string with theuser input, representing a basic semantic entity. For example, a slotfor “pizza” may be [SL:dish]. In particular embodiments, a set of validor expected named slots may be conditioned on the classified intent. Asan example and not by way of limitation, for [IN:play_music], a slot maybe [SL:song_name]. The semantic information aggregator 230 mayadditionally extract information from a social graph, a knowledge graph,and a concept graph, and retrieve a user's profile from the user contextengine 225. The semantic information aggregator 230 may further processinformation from these different sources by determining what informationto aggregate, annotating n-grams of the user input, ranking the n-gramswith confidence scores based on the aggregated information, formulatingthe ranked n-grams into features that can be used by the NLU module 220for understanding the user input. More information on aggregatingsemantic information may be found in U.S. patent application Ser. No.15/967,342, filed 30 Apr. 2018, which is incorporated by reference.Based on the output of the user context engine 225 and the semanticinformation aggregator 230, the NLU module 220 may identify a domain, anintent, and one or more slots from the user input in a personalized andcontext-aware manner. As an example and not by way of limitation, a userinput may comprise “show me how to get to the coffee shop”. The NLUmodule 220 may identify the particular coffee shop that the user wantsto go based on the user's personal information and the associatedcontextual information. In particular embodiments, the NLU module 220may comprise a lexicon of language and a parser and grammar rules topartition sentences into an internal representation. The NLU module 220may also comprise one or more programs that perform naive semantics orstochastic semantic analysis to the use of pragmatics to understand auser input. In particular embodiments, the parser may be based on a deeplearning architecture comprising multiple long-short term memory (LSTM)networks. As an example and not by way of limitation, the parser may bebased on a recurrent neural network grammar (RNNG) model, which is atype of recurrent and recursive LSTM algorithm. More information onnatural-language understanding may be found in U.S. patent applicationSer. No. 16/011,062, filed 18 Jun. 2018, U.S. patent application Ser.No. 16/025,317, filed 2 Jul. 2018, and U.S. patent application Ser. No.16/038,120, filed 17 Jul. 2018, each of which is incorporated byreference.

In particular embodiments, the identified domain, intent, and one ormore slots from the NLU module 220 may be sent to a dialog engine 235.In particular embodiments, the dialog engine 235 may manage the dialogstate and flow of the conversation between the user and the assistantxbot 215. The dialog engine 235 may additionally store previousconversations between the user and the assistant xbot 215. In particularembodiments, the dialog engine 235 may communicate with an entityresolution module 240 to resolve entities associated with the one ormore slots, which supports the dialog engine 235 to forward the flow ofthe conversation between the user and the assistant xbot 215. Inparticular embodiments, the entity resolution module 240 may access thesocial graph, the knowledge graph, and the concept graph when resolvingthe entities. Entities may include, for example, unique users orconcepts, each of which may have a unique identifier (ID). As an exampleand not by way of limitation, the knowledge graph may comprise aplurality of entities. Each entity may comprise a single recordassociated with one or more attribute values. The particular record maybe associated with a unique entity identifier. Each record may havediverse values for an attribute of the entity. Each attribute value maybe associated with a confidence probability. A confidence probabilityfor an attribute value represents a probability that the value isaccurate for the given attribute. Each attribute value may be alsoassociated with a semantic weight. A semantic weight for an attributevalue may represent how the value semantically appropriate for the givenattribute considering all the available information. For example, theknowledge graph may comprise an entity of a movie “The Martian” (2015),which includes information that has been extracted from multiple contentsources (e.g., Facebook, Wikipedia, movie review sources, mediadatabases, and entertainment content sources), and then deduped,resolved, and fused to generate the single unique record for theknowledge graph. The entity may be associated with a space attributevalue which indicates the genre of the movie “The Martian” (2015). Moreinformation on the knowledge graph may be found in U.S. patentapplication Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048,101, filed 27 Jul. 2018, each of which isincorporated by reference. The entity resolution module 240 mayadditionally request a user profile of the user associated with the userinput from the user context engine 225. In particular embodiments, theentity resolution module 240 may communicate with a privacy check module245 to guarantee that the resolving of the entities does not violateprivacy policies. In particular embodiments, the privacy check module245 may use an authorization/privacy server to enforce privacy policies.As an example and not by way of limitation, an entity to be resolved maybe another user who specifies in his/her privacy settings that his/heridentity should not be searchable on the online social network, and thusthe entity resolution module 240 may not return that user's identifierin response to a request. Based on the information obtained from thesocial graph, knowledge graph, concept graph, and user profile, andsubject to applicable privacy policies, the entity resolution module 240may therefore accurately resolve the entities associated with the userinput in a personalized and context-aware manner. In particularembodiments, each of the resolved entities may be associated with one ormore identifiers hosted by the social-networking system 160. As anexample and not by way of limitation, an identifier may comprise aunique user identifier (ID). In particular embodiments, each of theresolved entities may be also associated with a confidence score. Moreinformation on resolving entities may be found in U.S. patentapplication Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048,072, filed 27 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the dialog engine 235 may communicate withdifferent agents based on the identified intent and domain, and theresolved entities. In particular embodiments, an agent may be animplementation that serves as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. As an example and not byway of limitation, multiple device-specific implementations (e.g.,real-time calls for a client system 130 or a messaging application onthe client system 130) may be handled internally by a single agent.Alternatively, these device-specific implementations may be handled bymultiple agents associated with multiple domains. In particularembodiments, the agents may comprise first-party agents 250 andthird-party agents 255. In particular embodiments, first-party agents250 may comprise internal agents that are accessible and controllable bythe assistant system 140 (e.g. agents associated with services providedby the online social network). In particular embodiments, third-partyagents 255 may comprise external agents that the assistant system 140has no control over (e.g., music streams agents (Online Music App),ticket sales agent). The first-party agents 250 may be associated withfirst-party providers 260 that provide content objects and/or serviceshosted by the social-networking system 160. The third-party agents 255may be associated with third-party providers 265 that provide contentobjects and/or services hosted by the third-party system 170.

In particular embodiments, the communication from the dialog engine 235to the first-party agents 250 may comprise requesting particular contentobjects and/or services provided by the first-party providers 260. As aresult, the first-party agents 250 may retrieve the requested contentobjects from the first-party providers 260 and/or execute tasks thatcommand the first-party providers 260 to perform the requested services.In particular embodiments, the communication from the dialog engine 235to the third-party agents 255 may comprise requesting particular contentobjects and/or services provided by the third-party providers 265. As aresult, the third-party agents 255 may retrieve the requested contentobjects from the third-party providers 265 and/or execute tasks thatcommand the third-party providers 265 to perform the requested services.The third-party agents 255 may access the privacy check module 245 toguarantee no privacy violations before interacting with the third-partyproviders 265. As an example and not by way of limitation, the userassociated with the user input may specify in his/her privacy settingsthat his/her profile information is invisible to any third-party contentproviders. Therefore, when retrieving content objects associated withthe user input from the third-party providers 265, the third-partyagents 255 may complete the retrieval without revealing to thethird-party providers 265 which user is requesting the content objects.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, each of the first-party agents 250 orthird-party agents 255 may retrieve a user profile from the user contextengine 225 to execute tasks in a personalized and context-aware manner.As an example and not by way of limitation, a user input may comprise“book me a ride to the airport.” A transportation agent may execute thetask of booking the ride. The transportation agent may retrieve the userprofile of the user from the user context engine 225 before booking theride. For example, the user profile may indicate that the user preferstaxis, so the transportation agent may book a taxi for the user. Asanother example, the contextual information associated with the userprofile may indicate that the user is in a hurry so the transportationagent may book a ride from a ride-sharing service for the user since itmay be faster to get a car from a ride-sharing service than a taxicompany. In particular embodiment, each of the first-party agents 250 orthird-party agents 255 may take into account other factors whenexecuting tasks. As an example and not by way of limitation, otherfactors may comprise price, rating, efficiency, partnerships with theonline social network, etc.

In particular embodiments, the dialog engine 235 may communicate with aconversational understanding composer (CU composer) 270. The dialogengine 235 may send the requested content objects and/or the statuses ofthe requested services to the CU composer 270. In particularembodiments, the dialog engine 235 may send the requested contentobjects and/or the statuses of the requested services as a <k, c, u, d>tuple, in which k indicates a knowledge source, c indicates acommunicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer 270 maycomprise a natural-language generator (NLG) 271 and a user interface(UI) payload generator 272. The natural-language generator 271 maygenerate a communication content based on the output of the dialogengine 235. In particular embodiments, the NLG 271 may comprise acontent determination component, a sentence planner, and a surfacerealization component. The content determination component may determinethe communication content based on the knowledge source, communicativegoal, and the user's expectations. As an example and not by way oflimitation, the determining may be based on a description logic. Thedescription logic may comprise, for example, three fundamental notionswhich are individuals (representing objects in the domain), concepts(describing sets of individuals), and roles (representing binaryrelations between individuals or concepts). The description logic may becharacterized by a set of constructors that allow the natural-languagegenerator 271 to build complex concepts/roles from atomic ones. Inparticular embodiments, the content determination component may performthe following tasks to determine the communication content. The firsttask may comprise a translation task, in which the input to thenatural-language generator 271 may be translated to concepts. The secondtask may comprise a selection task, in which relevant concepts may beselected among those resulted from the translation task based on theuser model. The third task may comprise a verification task, in whichthe coherence of the selected concepts may be verified. The fourth taskmay comprise an instantiation task, in which the verified concepts maybe instantiated as an executable file that can be processed by thenatural-language generator 271. The sentence planner may determine theorganization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator 272 may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer 270 may communicatewith the privacy check module 245 to make sure the generation of thecommunication content follows the privacy policies. In particularembodiments, the CU composer 270 may retrieve a user profile from theuser context engine 225 when generating the communication content anddetermining the modality of the communication content. As a result, thecommunication content may be more natural, personalized, andcontext-aware for the user. As an example and not by way of limitation,the user profile may indicate that the user likes short sentences inconversations so the generated communication content may be based onshort sentences. As another example and not by way of limitation, thecontextual information associated with the user profile may indicatedthat the user is using a device that only outputs audio signals so theUI payload generator 272 may determine the modality of the communicationcontent as audio. More information on natural-language generation may befound in U.S. patent application Ser. No. 15/967,279, filed 30 Apr.2018, and U.S. patent application Ser. No. 15/966,455, filed 30 Apr.2018, each of which is incorporated by reference.

In particular embodiments, the CU composer 270 may send the generatedcommunication content to the assistant xbot 215. In particularembodiments, the assistant xbot 215 may send the communication contentto the messaging platform 205. The messaging platform 205 may furthersend the communication content to the client system 130 via theassistant application 136. In alternative embodiments, the assistantxbot 215 may send the communication content to a text-to-speech (TTS)module 275. The TTS module 275 may convert the communication content toan audio clip. The TTS module 275 may further send the audio clip to theclient system 130 via the assistant application 136.

In particular embodiments, the assistant xbot 215 may interact with aproactive inference layer 280 without receiving a user input. Theproactive inference layer 280 may infer user interests and preferencesbased on the user profile that is retrieved from the user context engine225. In particular embodiments, the proactive inference layer 280 mayfurther communicate with proactive agents 285 regarding the inference.The proactive agents 285 may execute proactive tasks based on theinference. As an example and not by way of limitation, the proactivetasks may comprise sending content objects or providing services to theuser. In particular embodiments, each proactive task may be associatedwith an agenda item. The agenda item may comprise a recurring item suchas a daily digest. The agenda item may also comprise a one-time item. Inparticular embodiments, a proactive agent 285 may retrieve the userprofile from the user context engine 225 when executing the proactivetask. Therefore, the proactive agent 285 may execute the proactive taskin a personalized and context-aware manner. As an example and not by wayof limitation, the proactive inference layer may infer that the userlikes the band Maroon 5 and the proactive agent 285 may generate arecommendation of Maroon 5's new song/album to the user.

In particular embodiments, the proactive agent 285 may generatecandidate entities associated with the proactive task based on a userprofile. The generation may be based on a straightforward backend queryusing deterministic filters to retrieve the candidate entities from astructured data store. The generation may be alternatively based on amachine-learning model that is trained based on the user profile, entityattributes, and relevance between users and entities. As an example andnot by way of limitation, the machine-learning model may be based onsupport vector machines (SVM). As another example and not by way oflimitation, the machine-learning model may be based on a regressionmodel. As another example and not by way of limitation, themachine-learning model may be based on a deep convolutional neuralnetwork (DCNN). In particular embodiments, the proactive agent 285 mayalso rank the generated candidate entities based on the user profile andthe content associated with the candidate entities. The ranking may bebased on the similarities between a user's interests and the candidateentities. As an example and not by way of limitation, the assistantsystem 140 may generate a feature vector representing a user's interestand feature vectors representing the candidate entities. The assistantsystem 140 may then calculate similarity scores (e.g., based on cosinesimilarity) between the feature vector representing the user's interestand the feature vectors representing the candidate entities. The rankingmay be alternatively based on a ranking model that is trained based onuser feedback data.

In particular embodiments, the proactive task may comprise recommendingthe candidate entities to a user. The proactive agent 285 may schedulethe recommendation, thereby associating a recommendation time with therecommended candidate entities. The recommended candidate entities maybe also associated with a priority and an expiration time. In particularembodiments, the recommended candidate entities may be sent to aproactive scheduler. The proactive scheduler may determine an actualtime to send the recommended candidate entities to the user based on thepriority associated with the task and other relevant factors (e.g.,clicks and impressions of the recommended candidate entities). Inparticular embodiments, the proactive scheduler may then send therecommended candidate entities with the determined actual time to anasynchronous tier. The asynchronous tier may temporarily store therecommended candidate entities as a job. In particular embodiments, theasynchronous tier may send the job to the dialog engine 235 at thedetermined actual time for execution. In alternative embodiments, theasynchronous tier may execute the job by sending it to other surfaces(e.g., other notification services associated with the social-networkingsystem 160). In particular embodiments, the dialog engine 235 mayidentify the dialog intent, state, and history associated with the user.Based on the dialog intent, the dialog engine 235 may select somecandidate entities among the recommended candidate entities to send tothe client system 130. In particular embodiments, the dialog state andhistory may indicate if the user is engaged in an ongoing conversationwith the assistant xbot 215. If the user is engaged in an ongoingconversation and the priority of the task of recommendation is low, thedialog engine 235 may communicate with the proactive scheduler toreschedule a time to send the selected candidate entities to the clientsystem 130. If the user is engaged in an ongoing conversation and thepriority of the task of recommendation is high, the dialog engine 235may initiate a new dialog session with the user in which the selectedcandidate entities may be presented. As a result, the interruption ofthe ongoing conversation may be prevented. When it is determined thatsending the selected candidate entities is not interruptive to the user,the dialog engine 235 may send the selected candidate entities to the CUcomposer 270 to generate a personalized and context-aware communicationcontent comprising the selected candidate entities, subject to theuser's privacy settings. In particular embodiments, the CU composer 270may send the communication content to the assistant xbot 215 which maythen send it to the client system 130 via the messaging platform 205 orthe TTS module 275. More information on proactively assisting users maybe found in U.S. patent application Ser. No. 15/967,193, filed 30 Apr.2018, and U.S. patent application Ser. No. 16/036,827, filed 16 Jul.2018, each of which is incorporated by reference.

In particular embodiments, the assistant xbot 215 may communicate with aproactive agent 285 in response to a user input. As an example and notby way of limitation, the user may ask the assistant xbot 215 to set upa reminder. The assistant xbot 215 may request a proactive agent 285 toset up such reminder and the proactive agent 285 may proactively executethe task of reminding the user at a later time.

In particular embodiments, the assistant system 140 may comprise asummarizer 290. The summarizer 290 may provide customized news feedsummaries to a user. In particular embodiments, the summarizer 290 maycomprise a plurality of meta agents. The plurality of meta agents mayuse the first-party agents 250, third-party agents 255, or proactiveagents 285 to generated news feed summaries. In particular embodiments,the summarizer 290 may retrieve user interests and preferences from theproactive inference layer 280. The summarizer 290 may then retrieveentities associated with the user interests and preferences from theentity resolution module 240. The summarizer 290 may further retrieve auser profile from the user context engine 225. Based on the informationfrom the proactive inference layer 280, the entity resolution module240, and the user context engine 225, the summarizer 290 may generatepersonalized and context-aware summaries for the user. In particularembodiments, the summarizer 290 may send the summaries to the CUcomposer 270. The CU composer 270 may process the summaries and send theprocessing results to the assistant xbot 215. The assistant xbot 215 maythen send the processed summaries to the client system 130 via themessaging platform 205 or the TTS module 275. More information onsummarization may be found in U.S. patent application Ser. No.15/967,290, filed 30 Apr. 2018, which is incorporated by reference.

FIG. 3 illustrates an example diagram flow of responding to a userrequest by the assistant system 140. In particular embodiments, theassistant xbot 215 may access a request manager 305 upon receiving theuser request. The request manager 305 may comprise a context extractor306 and a conversational understanding object generator (CU objectgenerator) 307. The context extractor 306 may extract contextualinformation associated with the user request. The context extractor 306may also update contextual information based on the assistantapplication 136 executing on the client system 130. As an example andnot by way of limitation, the update of contextual information maycomprise content items are displayed on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise alarm is set on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise a song is playing on the client system 130. TheCU object generator 307 may generate particular content objects relevantto the user request. The content objects may comprise dialog-sessiondata and features associated with the user request, which may be sharedwith all the modules of the assistant system 140. In particularembodiments, the request manager 305 may store the contextualinformation and the generated content objects in data store 310 which isa particular data store implemented in the assistant system 140.

In particular embodiments, the request manger 305 may send the generatedcontent objects to the NLU module 220. The NLU module 220 may perform aplurality of steps to process the content objects. At step 221, the NLUmodule 220 may generate a whitelist for the content objects. Inparticular embodiments, the whitelist may comprise interpretation datamatching the user request. At step 222, the NLU module 220 may perform afeaturization based on the whitelist. At step 223, the NLU module 220may perform domain classification/selection on user request based on thefeatures resulted from the featurization to classify the user requestinto predefined domains. The domain classification/selection results maybe further processed based on two related procedures. At step 224 a, theNLU module 220 may process the domain classification/selection resultusing an intent classifier. The intent classifier may determine theuser's intent associated with the user request. In particularembodiments, there may be one intent classifier for each domain todetermine the most possible intents in a given domain. As an example andnot by way of limitation, the intent classifier may be based on amachine-learning model that may take the domain classification/selectionresult as input and calculate a probability of the input beingassociated with a particular predefined intent. At step 224 b, the NLUmodule may process the domain classification/selection result using ameta-intent classifier. The meta-intent classifier may determinecategories that describe the user's intent. In particular embodiments,intents that are common to multiple domains may be processed by themeta-intent classifier. As an example and not by way of limitation, themeta-intent classifier may be based on a machine-learning model that maytake the domain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedmeta-intent. At step 225 a, the NLU module 220 may use a slot tagger toannotate one or more slots associated with the user request. Inparticular embodiments, the slot tagger may annotate the one or moreslots for the n-grams of the user request. At step 225 b, the NLU module220 may use a meta slot tagger to annotate one or more slots for theclassification result from the meta-intent classifier. In particularembodiments, the meta slot tagger may tag generic slots such asreferences to items (e.g., the first), the type of slot, the value ofthe slot, etc. As an example and not by way of limitation, a userrequest may comprise “change 500 dollars in my account to Japanese yen.”The intent classifier may take the user request as input and formulateit into a vector. The intent classifier may then calculate probabilitiesof the user request being associated with different predefined intentsbased on a vector comparison between the vector representing the userrequest and the vectors representing different predefined intents. In asimilar manner, the slot tagger may take the user request as input andformulate each word into a vector. The intent classifier may thencalculate probabilities of each word being associated with differentpredefined slots based on a vector comparison between the vectorrepresenting the word and the vectors representing different predefinedslots. The intent of the user may be classified as “changing money”. Theslots of the user request may comprise “500”, “dollars”, “account”, and“Japanese yen”. The meta-intent of the user may be classified as“financial service”. The meta slot may comprise “finance”.

In particular embodiments, the NLU module 220 may improve the domainclassification/selection of the content objects by extracting semanticinformation from the semantic information aggregator 230. In particularembodiments, the semantic information aggregator 230 may aggregatesemantic information in the following way. The semantic informationaggregator 230 may first retrieve information from the user contextengine 225. In particular embodiments, the user context engine 225 maycomprise offline aggregators 226 and an online inference service 227.The offline aggregators 226 may process a plurality of data associatedwith the user that are collected from a prior time window. As an exampleand not by way of limitation, the data may include news feedposts/comments, interactions with news feed posts/comments, Instagramposts/comments, search history, etc. that are collected from a prior90-day window. The processing result may be stored in the user contextengine 225 as part of the user profile. The online inference service 227may analyze the conversational data associated with the user that arereceived by the assistant system 140 at a current time. The analysisresult may be stored in the user context engine 225 also as part of theuser profile. In particular embodiments, both the offline aggregators226 and online inference service 227 may extract personalizationfeatures from the plurality of data. The extracted personalizationfeatures may be used by other modules of the assistant system 140 tobetter understand user input. In particular embodiments, the semanticinformation aggregator 230 may then process the retrieved information,i.e., a user profile, from the user context engine 225 in the followingsteps. At step 231, the semantic information aggregator 230 may processthe retrieved information from the user context engine 225 based onnatural-language processing (NLP). In particular embodiments, thesemantic information aggregator 230 may tokenize text by textnormalization, extract syntax features from text, and extract semanticfeatures from text based on NLP. The semantic information aggregator 230may additionally extract features from contextual information, which isaccessed from dialog history between a user and the assistant system140. The semantic information aggregator 230 may further conduct globalword embedding, domain-specific embedding, and/or dynamic embeddingbased on the contextual information. At step 232, the processing resultmay be annotated with entities by an entity tagger. Based on theannotations, the semantic information aggregator 230 may generatedictionaries for the retrieved information at step 233. In particularembodiments, the dictionaries may comprise global dictionary featureswhich can be updated dynamically offline. At step 234, the semanticinformation aggregator 230 may rank the entities tagged by the entitytagger. In particular embodiments, the semantic information aggregator230 may communicate with different graphs 330 including social graph,knowledge graph, and concept graph to extract ontology data that isrelevant to the retrieved information from the user context engine 225.In particular embodiments, the semantic information aggregator 230 mayaggregate the user profile, the ranked entities, and the informationfrom the graphs 330. The semantic information aggregator 230 may thensend the aggregated information to the NLU module 220 to facilitate thedomain classification/selection.

In particular embodiments, the output of the NLU module 220 may be sentto a co-reference module 315 to interpret references of the contentobjects associated with the user request. In particular embodiments, theco-reference module 315 may be used to identify an item to which theuser request refers. The co-reference module 315 may comprise referencecreation 316 and reference resolution 317. In particular embodiments,the reference creation 316 may create references for entities determinedby the NLU module 220. The reference resolution 317 may resolve thesereferences accurately. As an example and not by way of limitation, auser request may comprise “find me the nearest supermarket and direct methere”. The co-reference module 315 may interpret “there” as “thenearest supermarket”. In particular embodiments, the co-reference module315 may access the user context engine 225 and the dialog engine 235when necessary to interpret references with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution module 240 to resolve relevantentities. The entity resolution module 240 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution module 240 may comprise domain entity resolution 241 andgeneric entity resolution 242. The domain entity resolution 241 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 330. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 242 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 330. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof an electric car, the generic entity resolution 242 may resolve anelectric car as vehicle and the domain entity resolution 241 may resolvethe electric car as electric cars.

In particular embodiments, the output of the entity resolution module240 may be sent to the dialog engine 235 to forward the flow of theconversation with the user. The dialog engine 235 may comprise dialogintent resolution 236 and dialog state update/ranker 237. In particularembodiments, the dialog intent resolution 236 may resolve the userintent associated with the current dialog session based on dialoghistory between the user and the assistant system 140. The dialog intentresolution 236 may map intents determined by the NLU module 220 todifferent dialog intents. The dialog intent resolution 236 may furtherrank dialog intents based on signals from the NLU module 220, the entityresolution module 240, and dialog history between the user and theassistant system 140. In particular embodiments, the dialog stateupdate/ranker 237 may update/rank the dialog state of the current dialogsession. As an example and not by way of limitation, the dialog stateupdate/ranker 237 may update the dialog state as “completed” if thedialog session is over. As another example and not by way of limitation,the dialog state update/ranker 237 may rank the dialog state based on apriority associated with it.

In particular embodiments, the dialog engine 235 may communicate with atask completion module 335 about the dialog intent and associatedcontent objects. In particular embodiments, the task completion module335 may rank different dialog hypotheses for different dialog intents.The task completion module 335 may comprise an action selectioncomponent 336. In particular embodiments, the dialog engine 235 mayadditionally check against dialog policies 320 regarding the dialogstate. In particular embodiments, a dialog policy 320 may comprise adata structure that describes an execution plan of an action by an agent340. An agent 340 may select among registered content providers tocomplete the action. The data structure may be constructed by the dialogengine 235 based on an intent and one or more slots associated with theintent. A dialog policy 320 may further comprise multiple goals relatedto each other through logical operators. In particular embodiments, agoal may be an outcome of a portion of the dialog policy and it may beconstructed by the dialog engine 235. A goal may be represented by anidentifier (e.g., string) with one or more named arguments, whichparameterize the goal. As an example and not by way of limitation, agoal with its associated goal argument may be represented as{confirm_artist, args: {artist: “Madonna”}}. In particular embodiments,a dialog policy may be based on a tree-structured representation, inwhich goals are mapped to leaves of the tree. In particular embodiments,the dialog engine 235 may execute a dialog policy 320 to determine thenext action to carry out. The dialog policies 320 may comprise genericpolicy 321 and domain specific policies 322, both of which may guide howto select the next system action based on the dialog state. Inparticular embodiments, the task completion module 335 may communicatewith dialog policies 320 to obtain the guidance of the next systemaction. In particular embodiments, the action selection component 336may therefore select an action based on the dialog intent, theassociated content objects, and the guidance from dialog policies 320.

In particular embodiments, the output of the task completion module 335may be sent to the CU composer 270. In alternative embodiments, theselected action may require one or more agents 340 to be involved. As aresult, the task completion module 335 may inform the agents 340 aboutthe selected action. Meanwhile, the dialog engine 235 may receive aninstruction to update the dialog state. As an example and not by way oflimitation, the update may comprise awaiting agents' response. Inparticular embodiments, the CU composer 270 may generate a communicationcontent for the user using the NLG 271 based on the output of the taskcompletion module 335. In particular embodiments, the NLG 271 may usedifferent language models and/or language templates to generate naturallanguage outputs. The generation of natural language outputs may beapplication specific. The generation of natural language outputs may bealso personalized for each user. The CU composer 270 may also determinea modality of the generated communication content using the UI payloadgenerator 272. Since the generated communication content may beconsidered as a response to the user request, the CU composer 270 mayadditionally rank the generated communication content using a responseranker 273. As an example and not by way of limitation, the ranking mayindicate the priority of the response.

In particular embodiments, the output of the CU composer 270 may be sentto a response manager 325. The response manager 325 may performdifferent tasks including storing/updating the dialog state 326retrieved from data store 310 and generating responses 327. Inparticular embodiments, the output of CU composer 270 may comprise oneor more of natural-language strings, speech, actions with parameters, orrendered images or videos that can be displayed in a VR headset or ARsmart glass. As a result, the response manager 325 may determine whattasks to perform based on the output of CU composer 270. In particularembodiments, the generated response and the communication content may besent to the assistant xbot 215. In alternative embodiments, the outputof the CU composer 270 may be additionally sent to the TTS module 275 ifthe determined modality of the communication content is audio. Thespeech generated by the TTS module 275 and the response generated by theresponse manager 325 may be then sent to the assistant xbot 215.

Realtime Bandwidth-Based Communication

In particular embodiments, the content of Real-Time Communication (RTC)sessions such as Voice-over-Internet calls may be converted to contentin a communication modality having a more compact format that uses lessbandwidth. Voice calls may be converted to text using audio speechrecognition, for example. The compact format may be sent via acommunication network to a recipient device, which may convert thecontent in the compact modality back to the initial modality forpresentation to a recipient user. For example, the recipient device mayreceive the text and convert it to audio using a technique such astext-to-speech. The resulting audio may then be played on a speaker ofthe recipient device. In this way, a reproduction of the content in theinitial modality, e.g., speech, may be presented to the recipient usereven if there is insufficient bandwidth to send the content in theinitial modality. There may be differences between the initial andreproduced content, but the meaning of the reproduced content may besimilar to or the same as the initial content. The compact modality maybe any communication modality that can represent human-understandablecommunication using less data, and thus less network bandwidth, than theinitial modality. For example, text may represent words of a naturallanguage using less data than an audio recording of a person speakingthe words, and the audio encoding may in turn use less data than a videorecording of a person speaking the words. The voice or video calls maybe made using, e.g., a messaging application or any other suitablevoice/video communication service. Automated speech recognition andtext-to-speech functionality may be provided by a smart assistantarchitecture or any other suitable system.

In particular embodiments, modality conversions may be to modalitiesthat can represent fewer types of communication and use less bandwidth,or to modalities that can represent more types of communication and usemore bandwidth. For example, in the conversion of audio to text, audiocan directly represent words and sounds, while text can directlyrepresent words but not sounds (though sounds can be representedindirectly as words in text). In this conversion, sounds from the audiothat are not translated to text by automated speech recognition, such asintonations or other sounds, may be discarded, or may be added to thetext as supplemental content, e.g., as a description of the sound. Whenthe text is converted back to audio, the description in the text of thesupplemental content may be used to generate the corresponding sound,which may be added to the speech generated by a text-to-speechtechnique. Video can represent gestures that are not directlyrepresentable in audio (or text). Conversion from video to audio (ortext) may include discarding gesture content or adding supplementalaudio (or text) content that describes the gestures to the generatedaudio (or text). Subsequently, when the audio (or text) is converted tovideo, the supplemental content may be converted back to gestures, e.g.,by generating video animations of the gestures.

FIG. 4 illustrates an example network environment having links ofdifferent bandwidths. One or more client systems 130 a-c may communicatewith one or more client systems 430 a-c via network 110. Client systems130 a-c may communicate with network 110 via respective links 150 a-c,and client systems 430 a-c may communicate with network 110 viarespective links 450 a-c. Links 150 a and 450 a are of a first typehaving a first available bandwidth, e.g., 10 Mbit/sec links. Links 150 band 450 b are of a second type having a second available bandwidth,e.g., 100 Mbit/sec links. Links 150 c and 450 c are of a third typehaving a third available bandwidth, e.g., 1 Gbit/sec links. Clientsystem 130 a may communicate with client system 430 a via link 150 a,network 110, and link 450 a. As an example, the available bandwidth ofnetwork 110 may be 1 Gbit/sec. Thus, the available bandwidth between twoclient systems 130 may be limited to the bandwidth of the slowest of thelinks 150 a-c, 450 a-c between the two client systems 130. For example,the available bandwidth between client systems 130 a and 430 a may be100 Mbit/sec. As another example, the available bandwidth between clientsystems 130 b and 430 c may be 100 Mbit/sec because the bandwidth oflink 150 b is 100 Mb/sec. In another example, the available bandwidthbetween client systems 130 c and 430 c may be 1 Gbit/sec. The term“communication network” as used herein may refer to any combination ofthe network 110 and one or more of the links 150 a-c, 450 a-c.

FIG. 5 illustrates an example network environment in which clientsystems convert content between modalities for communication. Voice orvideo calls may be made between two people, referred to as a caller anda recipient. The caller may be a user of the first client system 130 andthe recipient may be a user of the second client system 430. The soundquality of such calls may be influenced by the bandwidth (e.g.,cellular, WIFI, and so on) of the underlying network 110 and links 150.Voice or video calls ordinarily encode a caller's voice as audio data onthe caller's client system 130 and send the audio data via the network110 to a recipient's client system 430. If network bandwidth issufficient for the voice calling protocol being used, e.g.,Voice-Over-IP (VOIP), then the call may proceed using that protocol.However, problems with the network 110 can result in parts of the audiodata not being received by the recipient's device, or being received toolate. Thus, when a voice call's network connection quality is poor,portions of the conversation may be difficult for the recipient tounderstand. Particular embodiments may detect low-bandwidth connectionsduring a voice call, use automated speech recognition to convert thecaller's speech to text, and send the text to the recipient's device viathe communication network 110 (instead of sending the audio data). Therecipient's client system 430 may then use text-to-speech to generateaudio from the text, and play the audio to the call recipient on theclient system 430's speaker. Since text is significantly smaller in sizethan audio data, the text is more likely to be received intact by therecipient's client system 430 when network bandwidth is low. Althoughthe recipient hears a computer-synthesized voice instead of the caller'svoice, the communication remains in the same voice modality instead ofswitching to a different modality, such as text messaging, in which thecaller and recipient would have to switch to typing instead of speaking.The recipient's client system 430 need not perform conversion back tothe original modality. Instead, the received communication may bepresented to the user in the same modality in which it is received. Forexample, the recipient's client system 430 may perform text-to-speech ifthe client system 430 is capable of text-to-speech. If the client system430 is not capable of performing text-to-speech, then the client system430 may display the received text displayed as a text message.

Referring to FIG. 5, a first client system 130 may communicate with asecond client system 432 via links 150 and network 110. Each clientsystem 130, 430 includes input devices 412 for receiving multi-modaluser input (such as voice, text, image, video) from a user, and outputdevices 477 for presenting multi-modal output to a user. Each clientsystem 130, 430 may also include an encoder 410 and a decoder 475, eachof which may convert content from a modality such as voice, text, image,video to a different modality. Encoder 410 may convert user input toproduce content in a different modality to be sent via network 110 toanother client system 430. For example, voice input received via amicrophone input device 412 may be converted to text by the encoder 410.Encoder 410 may be, e.g., an audio speech recognition module similar toASR module 210. The content produced by encoder 410 may be sent via thenetwork 110 from the first client system 130 to the second client system430. The second client system 430 may receive the content and usedecoder 475 to convert the received content to a different modality.Decoder 475 may be, e.g., a text-to-speech module similar to TTS module275. The second client system 430 may present the decoded content, e.g.,synthesized speech, to a user via output devices 477, e.g., a speaker.

Similarly to the communication from the first client system 130 to thesecond client system 430 described above, the second client system 430may send content such as user input to the first client system 130 usingencoder 410 to be decoded by the first client 130 using decoder 475. Thesecond client system 430 may receive user input, e.g., voice, via inputdevices 412 and convert the user input to content another modality,e.g., text, using encoder 410. The content may be sent to the firstclient system 130 via network 110. The first client system 130 mayreceive the content and convert it to a different modality, e.g., text,using decoder 475. The first client system 130 may present the contentin the different modality to a user using output devices 477. Althoughencoder 410 and decoder 475 are described as converting between specificmodalities, encoder 410 and decoder 475 may convert between any suitablemodalities. FIG. 6 is similar to FIG. 5, but shows ASR 210 as an exampleof encoder 410, and TTS 275 as an example of decoder 475.

In particular embodiments, network bandwidth may be divided into two ormore categories. In a two-category example, bandwidth may be categorizedas either good or bad (e.g., sufficient or too low). In thiscategorization, there are four possible cases for the bandwidthcombinations: (1) Good/Good, in which both the caller and recipient havegood network connectivity, (2) Bad/Bad, in which both the caller and therecipient have bad network connectivity, (3) Good/Bad or (4) Bad/Good,in which one has good connectivity and the other had bad connectivity.In particular embodiments, in the Good/Good case, the ordinarilycommunication mode (e.g., VOIP) may be used. In the Bad/Bad case, thecall content may be converted to a modality that uses less bandwidth.

In particular embodiments, in the Good/Bad or Bad/Good case, in whichone person has good connectivity and the other has bad connectivity, adetermination may be made based on an evaluation of how good or bad theconnectivity is. In these two cases, the call content may be convertedto a modality that uses less bandwidth if, for example, the availablebandwidth is insufficient for the ordinary communication mode. As anexample, a more granular assessment of the connectivity may be made andcompared to thresholds to determine whether to use the text mode. Theimplementation may depend on how frequently the network bandwidth can beevaluated. For example, if the bandwidth can be evaluated every 0.1seconds, then checks may be performed frequently, and a decision as towhether to use the text mode may be made substantially in real-time bycomparing the available bandwidth to a threshold bandwidth.

In particular embodiments, bandwidth evaluations may be aggregated overa period of time. For example, if out of the last 100 bandwidthevaluations (which may cover 10 seconds if they occur at 0.1 secondintervals), the average rate is X, then a decision may be madeadaptively, instead of potentially turning the text mode on or off every0.1 seconds. Thus, a decision may be made for longer periods of time, sothat larger chunks of conversation may be processed between switchingmodalities.

In particular embodiments, if the network bandwidth evaluations can onlybe performed once every few seconds (e.g., every 3, 5, or 10 seconds),then the text-mode decision may be based on the minimum bandwidthavailable during the last 10 second period. If there was a time duringthe last 10 second period where bandwidth was too low (e.g., below theminimum threshold), a switch to text mode may be made for the next 10second period. The communication experience should be above a certainminimum level. That is, if the frequency of evaluating bandwidth is Xseconds, the technique may look at the lowest bandwidth available inthat X seconds. The lowest bandwidth thus corresponds to the frequency(e.g., every X seconds).

As another example, if the bandwidth evaluations are expected to havehigh overhead, then an initial bandwidth evaluation may be performed. Ifthe initial evaluation finds that the bandwidth is good, then thefrequency of future checks may be limited. The frequency of checking maybe increased depending on a predicted likelihood of the quality beinggood. For example, if the device is in a location known to have lowbandwidth (e.g., poor cellular reception), then the frequency ofbandwidth evaluations may be increased, because an evaluation is morelikely to detect low bandwidth.

In particular embodiments, bandwidth may be evaluated by checking fordropped packets during a call, or via other bandwidth-monitoringtechniques. As another example, the client device may use its antennastrength as reported by the OS of the device to evaluate the bandwidth.The client device may report the antenna strength to the server, e.g.,at a particular frequency, while the call is in progress. For example,the client may send a single bit to the server (e.g., with each packet)indicating whether the bandwidth is sufficiently good. As anotherexample, the server may ping the client device to evaluate thebandwidth. In particular embodiments, if the caller passes through atunnel or other area that interferes with network communication, thenthe caller's device may capture the voice, store it in a buffer, and,when the caller leaves the tunnel and communication is restored, sendthe buffered data in a text mode. Particular embodiments may provide anindication that the communication modality has switched, e.g., from thecaller's real voice to a synthesized voice. The indication may be abeep, for example. Accessibility-related techniques may be used forrepresenting sounds that are not translatable to text. For example, acaller who is singing “Happy Birthday” to the recipient may receive avisual indication that the singing is being translated to text.Similarly, the recipient may receive such a visual indication. Anotherindication may be a color change on the recipient's display.

In particular embodiments, voice personalization may be used by thecaller's device to re-create the caller's voice. If the voice generatedby the text-to-speech sounds sufficiently similar to the caller's voice,then the call may remain in the text modality (e.g., continue to convertspeech to text) regardless of the bandwidth quality. The system maycontinue evaluating bandwidth while in the text mode. If the bandwidthmeets a threshold for acceptable audio quality, then the system mayswitch to the audio mode and stop using the text mode for theconversation.

In particular embodiments, the modality switching techniques disclosedherein may benefit callers and receivers using network 110 by being lessdisruptive than the existing system behavior when bandwidth is low, suchas intermittently choppy audio and dropped calls. Users making callsneed not perform the bandwidth evaluation themselves. That is, usersneed not monitor a signal strength indicator or listen for low-qualityaudio. Instead, users simply initiate a voice call, and the systemdetermines the available bandwidth and whether to switch to a differentmodality based on the available bandwidth.

In particular embodiments, network bandwidth may be divided into threecategories. Example conversions for three different input modalities(text, audio, or video) and three bandwidth categories (low, medium, orhigh) to three output modalities are shown in TABLE 1 below. Lowbandwidth may be, e.g., 1 Mbit/sec. Medium bandwidth may be, e.g., 10Mbit/sec. High bandwidth may be, e.g., 100 Mbit/sec or 1 Gbit/sec.

TABLE 1 Output Modality Based on Available Bandwidth Input modality LowMedium High Video Convert to text Convert to audio No change AudioConvert to text No change No change, or “upgrade” on server tovideo/animation (e.g., if processing on server is preferred overprocessing on destination client system) Text No change No change, or“upgrade” No change, or “upgrade” on server to audio on server tovideo/animation (e.g., if processing on server (e.g., if processing onserver is preferred over processing on is preferred over processing ondestination client system) destination client system)

The conversions shown in TABLE 1 are merely examples. Although thisdisclosure describes performing particular conversions in a particularmanner, this disclosure contemplates performing any suitable conversionsin any suitable manner.

Particular embodiments may perform automatic translation between naturallanguages by translating the text and speaking the translation to therecipient. Although such translation may be performed for a phone callthat uses network communication, a phone call is not necessary. Thetranslation may be performed by a handheld or wearable device that actsas a translator box by receiving the user's speech in a first language,performs automated speech recognition to generate text from the speech,translates the text to a second language, and speaks the translatedtext. The second language may be a preset or detected language, such asa language detected based on the device's location. The device mayperform real-time translation, or a close approximation thereof. Thelanguage preferences may be known to the clients and the server, e.g.,that the caller prefers English and the recipient prefers Spanish. Theclients may download language translation packages for particularlanguages as needed.

FIG. 7 illustrates a server 162 that converts content betweenmodalities. In particular embodiments, the automated speech recognition(ASR) and text-to-speech (TTS) operations may be performed on the user'sclient systems 130, 430 (e.g., phones). There is a tradeoff betweenbandwidth and processing capacity. If a client system's availablebandwidth is good or high, for example, then the server may perform thespeech-related processing. If the available bandwidth is bad, low, ormedium, for example, then the client system 130 may perform thespeech-related processing. Depending on how much processing capacity theclient system 130 has, and how much processing is needed for thespeech-related processing, the client system 130 may or may not besuited for performing the automated speech recognition processing. Ifthe device is not suited for the ASR processing, then the server 162 mayperform it.

In particular embodiments, if there is sufficient bandwidth between acaller's client system 130 and a server 162, then the automated speechrecognition may be performed on the server 162. Alternatively, if thereis sufficient bandwidth between the server 162 and the recipient'sclient system 430, the text-to-speech may be performed on the server162. Thus, the automated speech recognition or text-to-speech need notbe performed on the client system of the user who has sufficient networkbandwidth.

In particular embodiments, if sufficient bandwidth is available, contentin a compact modality such as text may be converted to content inanother modality such as audio that uses more storage space and thusmore bandwidth than the first modality. This up-conversion may upgradecontent to a modality that can represent more types of communication bygenerating animations such as avatars, emoji, facial expressions, or thelike. The conversion may be performed on a server system or on thedestination client system. Conversion on the server system may bedesirable when the server has greater available processing capacity thanthe client system. Alternatively or additionally, text or audio may besent to the destination client system 430 by the server and converted onthe destination client system.

Referring to FIG. 7, the server 162 may include a decoder 475, which mayperform conversion between modalities. The decoder 475 on server 162 maybe used to perform modality conversion instead of a decoder 476 on thefirst client system 130 or the second client system 430. Further, theserver 162 may include an encoder 410, which may be used to performmodality conversion instead of an encoder 410 on the first client system130 or the second client system 430. The decoder 475 or encoder 410 maybe used when, for example, conversion on the server 162 is moreefficient than conversion on one or both of the client systems 130, 430,or when performing conversion on one or both of the client systems 130,430 is not desirable, e.g., because of limited processing poweravailable on one or both of the client systems 130, 430. Although thisdisclosure describes converting content between modalities in aparticular manner, this disclosure contemplates converting contentbetween modalities in any suitable manner.

FIGS. 8 and 9 illustrate example network environments in which clientsystems convert speech to and from text for communication associatedwith an assistant system. FIG. 8 is similar to FIG. 6, but shows ASR 210and TTS 275 as modules that may communicate with client systems 130, 430and with each other via network 110. The automated speech recognitionand text-to-speech may be provided by the Assistant architecture. TheAssistant system may utilize user profile information from (e.g., storedin a user context engine) to aid both in parsing speech via ASR (e.g.,to more accurately parse spoken words to text) and generatingsynthesized speech via TTS (e.g., to generate synthetized speech thatsounds more like the user).

Referring to FIG. 9, ASR 210 may communicate with TTS 275 via amessaging platform 205. The messaging platform 205 may send messages viaa network 110 (not shown). The messages may contain content (such asvoice, text, image, video), which may be converted to differentmodalities by modules such as ASR 210 and TTS 275. Although thisdisclosure describes using modules to convert between modalities in aparticular manner, this disclosure contemplates using modules to convertbetween modalities in any suitable manner.

FIG. 10 illustrates example conversions between content of differentcommunication types associated with different modalities. A table 1000of size-reducing modality conversions lists three example modalities:video 1002, audio 1008, and text 1010. The conversions shown in table1020 are to destination modalities that may use less data and candirectly represent fewer types of communication than are directlyrepresentable in the source modality. Video 1002 can representvocalizations 1004 and gestures 1006. Audio 1008 can representvocalizations 1004. Text 1010 can represent words 1012. Vocalizationsmay be understood as an additional type of communication that caninclude intonations and other sounds not directly represented in words1012.

A conversion from video 1002 to audio 1008 may involve conversion from acombination of vocalizations 1004 and gestures 1006 to vocalizations1004. In this conversion, the gestures 1006 may be discarded, or anaudio representation of the gestures may be generated and added to thegenerated audio 1008 as supplemental content, e.g., as an audiodescription of the gesture, such as “shrug” at the time in the generatedaudio 1008 that corresponds to a time in the video 1002 at which aperson shrugs. The reverse conversion, conversion from audio 1008 tovideo 1002 may involve converting from vocalizations 1004 andsupplemental content (if any) to a combination of vocalizations 1004 andgestures 1006. Gestures 1006 may be additional type of communicationthat can include non-verbal communication, e.g., facial expressions,body language, and so on, not directly representable in words 1012 orvocalizations 1004. If the audio includes the word “shrug” then ananimation of a person shrugging maybe included in the generated video1002 at a time in the video 1002 that corresponds to the time in theaudio 1008 at which the word “shrug” occurs. The word “shrug” may havebeen included as supplemental content by the conversion from video 1002to audio 1008 as described above. Alternatively or additionally, theword “shrug” may be spoken in the audio 1008 (without being added assupplemental content), and the audio to video conversion may stillgenerate an animation of a person shrugging and include the animation inthe generated video 1002 at the corresponding time.

A conversion from audio 1008 to text 1010 may involve conversion fromvocalizations 1004 to words 1012. In this conversion, sounds from theaudio that are not translated to text by automated speech recognition,such as intonations or other sounds, may be discarded, or may be addedto the text as supplemental content, e.g., as a description of thesound, such as “hiccup.” When the text is converted back to audio, thedescription in the text of the supplemental content, e.g., “hiccup,” maybe used to generate the corresponding sound, which may be added to thespeech generated by a text-to-speech technique.

In particular embodiments, each modality in table 1000 is associatedwith one or more communication types. The communication types, whichinclude vocalizations 1004, gestures 1006, and words 1012, may beunderstood as types of human communication that can be represented usingthe associated modalities. Vocalizations 1004 may correspond to spokenlanguage or other sounds. Gestures 1006 may correspond to non-verbalcommunication, such as facial gestures, hand gestures, or other bodygestures (e.g., shrugs). Words 1012 may correspond to text, e.g.,written language. Video 1002 may be, e.g., video clips, and mayrepresent vocalizations 1004 and gestures 1006. The vocalizations 1004may be independent of or associated with the gestures 1006. In aparticular communication, certain instances of vocalizations 1004 mayhave no associated instances of gestures 1006, and vice versa. A phrasemay be spoken by a person without any accompanying gestures, and agesture may be made without any accompanying speech. Other instances ofvocalizations 1004 may have associated gestures 1006, e.g., spokenphrase may be accompanied by a simultaneous hand-waving gesture. Audio1008 may be, e.g., audio clips of recorded speech, and may representvocalizations 1004. Text 1010 may be a string of characters, e.g., “Whattime is it?”

In particular embodiments, video 1002 may be converted to audio 1008 byremoving the gestures 1006 (and any other images) from the video 1002,or by copying the vocalizations 1004 but not the gestures 1005 from thevideo 1002. The amount of data needed to represent a particularcommunication as video may be greater than the amount of data needed torepresent the communication as audio, since video images use asubstantial quantity of data that is not needed for audio. Converting aparticular communication from video to audio may result in loss of imagecontent that is present in the video, such as video frames of gestures1006, which may be removed by the conversion. Thus, conversion from afirst modality to a second modality that uses less data may lose aportion of the meaning present in the content in the first modality. Forexample, the meaning of gestures 1006 that are present in the firstmodality may not be present in the second modality. To reduce the amountof meaning lost in the conversion, an additional conversion techniquemay be used. For example, gestures 1006 may be converted tovocalizations 1004 by automatically recognizing the gestures in thevideo 1002, generating additional vocalizations (e.g., synthesizedspeech) that describes the gestures, and including the additionalvocalizations in the vocalizations 1004.

In particular embodiments, audio 1008 may be converted to text 1010using ASR 210, for example. ASR 210 may generate words 1012 based on thevocalizations 1004. The amount of data needed to represent a particularcommunication as audio may be greater than the amount of data needed torepresent the communication as text 1010, since audio uses a substantialquantity of data to represent vocalizations 1004 that is not needed torepresent words 1012. Converting a particular communication from audioto text may result in loss of sound content that is present in thevocalizations 1004, such as intonations or other sounds, which may beremoved by the conversion to words 1012 by ASR 210. As described above,conversion from a first modality to a second modality that uses lessdata may lose a portion of the meaning present in the content in thefirst modality. For example, the meaning of certain vocalizations 1006that are present in the first modality, such as intonations or othersounds, may not be present in the second modality. To reduce the amountof meaning lost in the conversion, an additional conversion techniquemay be used. For example, intonations or other sounds may optionally beconverted to words 1012, e.g., by automatically recognizing theintonations or other sounds, generating additional words that describesthe intonations or other sounds, and including the additional words inthe words 1012.

Although this disclosure describes converting from modalities havinglarger content sizes to modalities having smaller content sizes in aparticular manner, this disclosure contemplates converting frommodalities having larger content sizes to modalities having smallercontent sizes in any suitable manner. As an example and not by way oflimitation, video 1002 may be converted to text 1010 without anintermediate conversion to audio 1008, e.g., by using ASR 210 on theaudio portion of the video 1002.

Referring again to FIG. 10, a table 1020 of size-increasing modalityconversions lists three example modalities: text 1010, audio 1008, andvideo 1002. The conversions shown in table 1020 are to destinationmodalities that may use more data and can directly represent additionaltypes of communication not directly representable in the sourcemodality. A conversion from text 1010 to audio 1008 may involveconverting from words 1012 to vocalizations 1004. A conversion fromaudio 1008 to video 1002 may involve converting from vocalizations 1004to vocalizations 1004 (which may be the same as the vocalizations 1004in the audio 1008) and gestures 1006.

Content for the additional type of communication may optionally begenerated based on the content being converted using an additionalconversion technique. Such generated content may be referred to hereinas supplemental content. Types of communication that are not directlyrepresentable in a particular modality may be represented indirectly inthat modality. The indirect representation may be converted to a directrepresentation or approximation in another modality. Text can representor at least approximate vocalizations using words, such as “laugh” or“hiccup” that can be converted to vocalizations such as intonation orother sounds. Thus, vocalizations 1004 may be generated based onrepresentations or approximations of vocalizations in words 1012, asshown by the dotted line from the words 1012 to the vocalizations 1004in table 1020. Text can also represent or approximate gestures usingcertain words, such as “shrug” or “smile” that can be converted to thecorresponding gestures. Vocalizations can also represent or approximategestures using sounds such as the sound of laughter, which can beconverted to corresponding facial expressions. Thus, gestures 1006 maybe generated based on representations or approximations of gestures invocalizations 1004 as shown by the dotted line from the vocalizations1004 to the gestures 1006, or based on representations or approximationsof gestures in words 1012 (not shown).

Text 1010 may be converted to audio 1008 using TTS 275, for example. TTS275 may generate synthesized vocalizations 1004 based on the words 1012of the text 1010. Audio can directly represent additional meaning usingintonation and other sounds. Thus an additional conversion technique maybe used to generate supplemental content for the modality that canrepresent an additional type of communication. Converting a particularcommunication from text to audio may thus include generating intonationand other sounds based on the text to include in the audio. For example,if a sentence of text ends in a question mark, the TTS 275 may increasethe pitch of the synthesized words near the end of the sentence to mimicvocalization of a question.

In particular embodiments, audio 1008 may be converted to video 1002 byusing the audio 1008 as an audio track in the video 1008 so that thevocalizations 1004 are included in the video. Optionally, supplementalcontent for gestures 1006, such as animations of facial gestures, may beautomatically generated based on the vocalizations or on the words 1020that are vocalized. The generated gestures 1006 may be included in thevideo 1002.

Although this disclosure describes converting to modalities havingadditional types of communication in a particular manner, thisdisclosure contemplates converting to modalities having additional typesof communication in any suitable manner. As an example and not by way oflimitation, text 1010 may be converted to video 1002 without anintermediate conversion to audio 1008, e.g., by using TTS 275 on thetext 1010 to generate the audio portion of the video 1002, andoptionally generating video, e.g., animations, based on the text 1010.

FIG. 11 illustrates an example method 1100 for communication byconverting between different modalities. The method may begin at step1110, where the assistant system 140 may receive, from a first clientsystem associated with a first user during a communication sessionbetween the first client system and a second client system associatedwith a second user, a first user communication in a first modality,wherein the first user communication is to be sent via the communicationnetwork to the second client system. At step 1120, the assistant system140 may determine an available bandwidth of the communication networkwith respect to the second client system. At step 1130, the assistantsystem 140 may determine a second modality to send the first usercommunication to the second client system, wherein the second modalityis a suitable output modality determined based on the availablebandwidth of the communication network with respect to the second clientsystem, and wherein the first and second modalities are differentmodalities. At step 1140, the assistant system 140 may generate a seconduser communication in the second modality by converting the first usercommunication to the second modality. At step 1150, the assistant system140 may send, to the second client system responsive to the first usercommunication, the second user communication for presentation to thesecond user. At step 1160, the assistant system 140 may receive, at thesecond client system, the second user communication. At step 1170, theassistant system 140 may generate, by the second client system a thirduser communication in the first modality by converting the second usercommunication to the first modality.

Particular embodiments may repeat one or more steps of the method ofFIG. 11, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 11 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 11 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method forcommunication by converting between different modalities including theparticular steps of the method of FIG. 11, this disclosure contemplatesany suitable method for communication by converting between differentmodalities including any suitable steps, which may include all, some, ornone of the steps of the method of FIG. 11, where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method of FIG. 11, this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method of FIG. 11.

In particular embodiments, the assistant system 140 may receive, from afirst client system associated with a first user during a communicationsession between the first client system and a second client systemassociated with a second user, a first user communication in a firstmodality, wherein the first user communication is to be sent via thecommunication network to the second client system. The first modalitymay be video, audio, text, an image, or the like. The first usercommunication comprises a video clip, an audio clip, a text string, orother suitable type of content.

In particular embodiments, the assistant system 140 may determine, bythe one or more computing systems, an available bandwidth of thecommunication network with respect to the second client system. The oneor more computing systems may include the first client system. Forexample, the first client system may perform at least one of theoperations associated with the assistant system 140. As an example andnot by way of limitation, the available bandwidth may be an estimate ofan amount of bandwidth available on the communications network for useby the first client system. As an example and not by way of limitation,the communication session may be associated with a network interfacethat communicates with the communications network, and the availablebandwidth is determined based on one or more attributes of the networkinterface. The attributes may include a network type or networkbandwidth. The available bandwidth may be determined by sending apredetermined quantity of data via the communications network to adestination address, and measuring a rate at which the predeterminedquantity of data is sent.

In particular embodiments, the assistant system 140 may determine, bythe one or more computing systems, a second modality to send the firstuser communication to the second client system, wherein the secondmodality is a suitable output modality determined based on the availablebandwidth of the communication network with respect to the second clientsystem, and wherein the first and second modalities are differentmodalities.

In particular embodiments, the assistant system 140 may determine thesecond modality by determining whether the available bandwidth issufficient to send the first user communication to the second clientsystem in the first modality, and when the available bandwidth isdetermined to be insufficient to send the first user communication tothe second client system in the first modality, determining the secondmodality such that the available bandwidth of the communication networkis sufficient to send the second user communication in the secondmodality. As an example and not by way of limitation, the first modalitymay be video, and the available bandwidth maybe insufficient to send thefirst user communication as video, and (1) the second modality is audiowhen the available bandwidth is sufficient to send the first usercommunication as audio, or (b) the second modality is text when theavailable bandwidth is insufficient to send the first user communicationas audio. The second modality may use more bandwidth than the firstmodality, and the available bandwidth of the communication network issufficient to send the second user communication. As an example and notby way of limitation, the first modality may be text and the secondmodality may be audio, and converting the first user communication tothe second modality may include converting the first user communicationfrom text to audio-encoded speech, where the second user communicationincludes the audio-encoded speech. As an example and not by way oflimitation, the first modality may be text and the second modality maybe video, and converting the first user communication to the secondmodality may include converting the first user communication from textto video, the video including audio-encoded speech and animation basedon the text, where the second user communication includes the video. Theanimation may include one or more avatars, emoji, or facial expressionsbased on the text.

As an example and not by way of limitation, the first modality may beaudio and the second modality may be video, and converting the firstuser communication to the second modality may include converting thefirst user communication from audio to video including audio-encodedspeech and animation based on the audio, where the second usercommunication includes the video.

In particular embodiments, the assistant system 140 may generate, by theone or more computing systems, a second user communication in the secondmodality by converting the first user communication to the secondmodality. In particular embodiments, the assistant system 140 may send,to the second client system responsive to the first user communication,the second user communication for presentation in the second modality tothe second user.

In particular embodiments, the assistant system 140 may receive, at thesecond client system, the second user communication and generating, bythe second client system a third user communication in the firstmodality by converting the second user communication to the firstmodality.

Social Graphs

FIG. 12 illustrates an example social graph 1200. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 1200 in one or more data stores. In particularembodiments, the social graph 1200 may include multiple nodes—which mayinclude multiple user nodes 1202 or multiple concept nodes 1204—andmultiple edges 1206 connecting the nodes. Each node may be associatedwith a unique entity (i.e., user or concept), each of which may have aunique identifier (ID), such as a unique number or username. The examplesocial graph 1200 illustrated in FIG. 12 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, a client system 130, anassistant system 140, or a third-party system 170 may access the socialgraph 1200 and related social-graph information for suitableapplications. The nodes and edges of the social graph 1200 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 1200.

In particular embodiments, a user node 1202 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 1202 correspondingto the user, and store the user node 1202 in one or more data stores.Users and user nodes 1202 described herein may, where appropriate, referto registered users and user nodes 1202 associated with registeredusers. In addition or as an alternative, users and user nodes 1202described herein may, where appropriate, refer to users that have notregistered with the social-networking system 160. In particularembodiments, a user node 1202 may be associated with informationprovided by a user or information gathered by various systems, includingthe social-networking system 160. As an example and not by way oflimitation, a user may provide his or her name, profile picture, contactinformation, birth date, sex, marital status, family status, employment,education background, preferences, interests, or other demographicinformation. In particular embodiments, a user node 1202 may beassociated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 1202 maycorrespond to one or more web interfaces.

In particular embodiments, a concept node 1204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node1204 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 1204 may be associated with one or more dataobjects corresponding to information associated with concept node 1204.In particular embodiments, a concept node 1204 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 1200 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 1120.Profile interfaces may also be hosted on third-party websites associatedwith a third-party system 170. As an example and not by way oflimitation, a profile interface corresponding to a particular externalweb interface may be the particular external web interface and theprofile interface may correspond to a particular concept node 1204.Profile interfaces may be viewable by all or a selected subset of otherusers. As an example and not by way of limitation, a user node 1202 mayhave a corresponding user-profile interface in which the correspondinguser may add content, make declarations, or otherwise express himself orherself. As another example and not by way of limitation, a concept node1204 may have a corresponding concept-profile interface in which one ormore users may add content, make declarations, or express themselves,particularly in relation to the concept corresponding to concept node1204.

In particular embodiments, a concept node 1204 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 1202 corresponding to the user and a conceptnode 1204 corresponding to the third-party web interface or resource andstore edge 1206 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 1200 maybe connected to each other by one or more edges 1206. An edge 1206connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 1206 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 1206 connecting the first user's user node 1202 to thesecond user's user node 1202 in the social graph 1200 and store edge1206 as social-graph information in one or more of data stores 1612. Inthe example of FIG. 12, the social graph 1200 includes an edge 1206indicating a friend relation between user nodes 1202 of user “A” anduser “B” and an edge indicating a friend relation between user nodes1202 of user “C” and user “B.” Although this disclosure describes orillustrates particular edges 1206 with particular attributes connectingparticular user nodes 1202, this disclosure contemplates any suitableedges 1206 with any suitable attributes connecting user nodes 1202. Asan example and not by way of limitation, an edge 1206 may represent afriendship, family relationship, business or employment relationship,fan relationship (including, e.g., liking, etc.), follower relationship,visitor relationship (including, e.g., accessing, viewing, checking-in,sharing, etc.), subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in the social graph 1200 byone or more edges 1206.

In particular embodiments, an edge 1206 between a user node 1202 and aconcept node 1204 may represent a particular action or activityperformed by a user associated with user node 1202 toward a conceptassociated with a concept node 1204. As an example and not by way oflimitation, as illustrated in FIG. 12, a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “watched” a concept,each of which may correspond to an edge type or subtype. Aconcept-profile interface corresponding to a concept node 1204 mayinclude, for example, a selectable “check in” icon (such as, forexample, a clickable “check in” icon) or a selectable “add to favorites”icon. Similarly, after a user clicks these icons, the social-networkingsystem 160 may create a “favorite” edge or a “check in” edge in responseto a user's action corresponding to a respective action. As anotherexample and not by way of limitation, a user (user “C”) may listen to aparticular song (“Imagine”) using a particular application (Online MusicApp, which is an online music application). In this case, thesocial-networking system 160 may create a “listened” edge 1206 and a“used” edge (as illustrated in FIG. 12) between user nodes 1202corresponding to the user and concept nodes 1204 corresponding to thesong and application to indicate that the user listened to the song andused the application. Moreover, the social-networking system 160 maycreate a “played” edge 1206 (as illustrated in FIG. 12) between conceptnodes 1204 corresponding to the song and the application to indicatethat the particular song was played by the particular application. Inthis case, “played” edge 1206 corresponds to an action performed by anexternal application (Online Music App) on an external audio file (thesong “Imagine”). Although this disclosure describes particular edges1206 with particular attributes connecting user nodes 1202 and conceptnodes 1204, this disclosure contemplates any suitable edges 1206 withany suitable attributes connecting user nodes 1202 and concept nodes1204. Moreover, although this disclosure describes edges between a usernode 1202 and a concept node 1204 representing a single relationship,this disclosure contemplates edges between a user node 1202 and aconcept node 1204 representing one or more relationships. As an exampleand not by way of limitation, an edge 1206 may represent both that auser likes and has used at a particular concept. Alternatively, anotheredge 1206 may represent each type of relationship (or multiples of asingle relationship) between a user node 1202 and a concept node 1204(as illustrated in FIG. 12 between user node 1202 for user “E” andconcept node 1204 for “Online Music App”).

In particular embodiments, the social-networking system 160 may createan edge 1206 between a user node 1202 and a concept node 1204 in thesocial graph 1200. As an example and not by way of limitation, a userviewing a concept-profile interface (such as, for example, by using aweb browser or a special-purpose application hosted by the user's clientsystem 130) may indicate that he or she likes the concept represented bythe concept node 1204 by clicking or selecting a “Like” icon, which maycause the user's client system 130 to send to the social-networkingsystem 160 a message indicating the user's liking of the conceptassociated with the concept-profile interface. In response to themessage, the social-networking system 160 may create an edge 1206between user node 1202 associated with the user and concept node 1204,as illustrated by “like” edge 1206 between the user and concept node1204. In particular embodiments, the social-networking system 160 maystore an edge 1206 in one or more data stores. In particularembodiments, an edge 1206 may be automatically formed by thesocial-networking system 160 in response to a particular user action. Asan example and not by way of limitation, if a first user uploads apicture, watches a movie, or listens to a song, an edge 1206 may beformed between user node 1202 corresponding to the first user andconcept nodes 1204 corresponding to those concepts. Although thisdisclosure describes forming particular edges 1206 in particularmanners, this disclosure contemplates forming any suitable edges 1206 inany suitable manner.

Vector Spaces and Embeddings

FIG. 13 illustrates an example view of a vector space 1300. Inparticular embodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 1300 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 1300 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 1300 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 1300(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 1310, 1320, and 1330 may be represented as pointsin the vector space 1300, as illustrated in FIG. 13. An n-gram may bemapped to a respective vector representation. As an example and not byway of limitation, n-grams t₁ and t₂ may be mapped to vectors

and

in the vector space 1300, respectively, by applying a function {rightarrow over (π)} defined by a dictionary, such that

=

(t₁) and

=

(t₂). As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a model, such as Word2vec, may be used tomap an n-gram to a vector representation in the vector space 1300. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 1300 by using a machine leaning model(e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 1300 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors

and

in the vector space 1300, respectively, by applying a function

, such that

={right arrow over (π)}(e₁) and

=

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function may map objects to vectors by feature extraction,which may start from an initial set of measured data and build derivedvalues (e.g., features). As an example and not by way of limitation, anobject comprising a video or an image may be mapped to a vector by usingan algorithm to detect or isolate various desired portions or shapes ofthe object. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function {right arrow over (π)}may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction {right arrow over (π)} may map an object e to a vector

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 1300. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{\overset{\rightharpoonup}{v_{1}} \cdot \overset{\rightharpoonup}{v_{2}}}{{\overset{\rightharpoonup}{v_{1}}}\mspace{11mu}{\overset{\rightharpoonup}{v_{2}}}}.$As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 1300. As an example and not by way of limitation,vector 1310 and vector 1320 may correspond to objects that are moresimilar to one another than the objects corresponding to vector 1310 andvector 1330, based on the distance between the respective vectors.Although this disclosure describes calculating a similarity metricbetween vectors in a particular manner, this disclosure contemplatescalculating a similarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 14 illustrates an example artificial neural network (“ANN”) 1400.In particular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 1400 may comprise an inputlayer 1410, hidden layers 1420, 1430, 1460, and an output layer 1450.Each layer of the ANN 1400 may comprise one or more nodes, such as anode 1405 or a node 1415. In particular embodiments, each node of an ANNmay be connected to another node of the ANN. As an example and not byway of limitation, each node of the input layer 1410 may be connected toone of more nodes of the hidden layer 1420. In particular embodiments,one or more nodes may be a bias node (e.g., a node in a layer that isnot connected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 14 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 14 depicts a connection between each node of the inputlayer 1410 and each node of the hidden layer 1420, one or more nodes ofthe input layer 1410 may not be connected to one or more nodes of thehidden layer 1420.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 1420 may comprise the output of one or morenodes of the input layer 1410. As another example and not by way oflimitation, the input to each node of the output layer 1450 may comprisethe output of one or more nodes of the hidden layer 1460. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$the rectifier F_(k) (s_(k))=max (0,s_(k)), or any other suitablefunction F_(k)(s_(k)), where s_(k) may be the effective input to node k.In particular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection1425 between the node 1405 and the node 1415 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 1405 is used as an input to the node 1415. As anotherexample and not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k) (s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 1400 and an expected output. As another example andnot by way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 1204 corresponding to a particular photo mayhave a privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 1200. A privacy setting may bespecified for one or more edges 1206 or edge-types of the social graph1200, or with respect to one or more nodes 1202, 1204 or node-types ofthe social graph 1200. The privacy settings applied to a particular edge1206 connecting two nodes may control whether the relationship betweenthe two entities corresponding to the nodes is visible to other users ofthe online social network. Similarly, the privacy settings applied to aparticular node may control whether the user or concept corresponding tothe node is visible to other users of the online social network. As anexample and not by way of limitation, a first user may share an objectto the social-networking system 160. The object may be associated with aconcept node 1204 connected to a user node 1202 of the first user by anedge 1206. The first user may specify privacy settings that apply to aparticular edge 1206 connecting to the concept node 1204 of the object,or may specify privacy settings that apply to all edges 1206 connectingto the concept node 1204. As another example and not by way oflimitation, the first user may share a set of objects of a particularobject-type (e.g., a set of images). The first user may specify privacysettings with respect to all objects associated with the first user ofthat particular object-type as having a particular privacy setting(e.g., specifying that all images posted by the first user are visibleonly to friends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such voice recording may be used onlyfor a limited purpose (e.g., authentication, tagging the user inphotos), and further specify that such voice recording may not be sharedwith any third-party system 170 or used by other processes orapplications associated with the social-networking system 160.

Systems and Methods

FIG. 15 illustrates an example computer system 1500. In particularembodiments, one or more computer systems 1500 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1500 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1500 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1500.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1500. This disclosure contemplates computer system 1500 taking anysuitable physical form. As example and not by way of limitation,computer system 1500 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1500 may include one or more computersystems 1500; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1500 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1500 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1500 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1500 includes a processor1502, memory 1504, storage 1506, an input/output (I/O) interface 1508, acommunication interface 1510, and a bus 1512. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1502 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1502 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1504, or storage 1506; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1504, or storage 1506. In particularembodiments, processor 1502 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1502 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1502 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1504 or storage 1506, and the instruction caches may speed upretrieval of those instructions by processor 1502. Data in the datacaches may be copies of data in memory 1504 or storage 1506 forinstructions executing at processor 1502 to operate on; the results ofprevious instructions executed at processor 1502 for access bysubsequent instructions executing at processor 1502 or for writing tomemory 1504 or storage 1506; or other suitable data. The data caches mayspeed up read or write operations by processor 1502. The TLBs may speedup virtual-address translation for processor 1502. In particularembodiments, processor 1502 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1502 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1502 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1502. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1504 includes main memory for storinginstructions for processor 1502 to execute or data for processor 1502 tooperate on. As an example and not by way of limitation, computer system1500 may load instructions from storage 1506 or another source (such as,for example, another computer system 1500) to memory 1504. Processor1502 may then load the instructions from memory 1504 to an internalregister or internal cache. To execute the instructions, processor 1502may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1502 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1502 may then write one or more of those results to memory 1504. Inparticular embodiments, processor 1502 executes only instructions in oneor more internal registers or internal caches or in memory 1504 (asopposed to storage 1506 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1504 (asopposed to storage 1506 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1502 to memory 1504. Bus 1512 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1502 and memory 1504and facilitate accesses to memory 1504 requested by processor 1502. Inparticular embodiments, memory 1504 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1504 may include one ormore memories 1504, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1506 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1506 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1506 may include removable or non-removable (or fixed)media, where appropriate. Storage 1506 may be internal or external tocomputer system 1500, where appropriate. In particular embodiments,storage 1506 is non-volatile, solid-state memory. In particularembodiments, storage 1506 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1506taking any suitable physical form. Storage 1506 may include one or morestorage control units facilitating communication between processor 1502and storage 1506, where appropriate. Where appropriate, storage 1506 mayinclude one or more storages 1506. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1508 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1500 and one or more I/O devices. Computersystem 1500 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1500. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1508 for them. Where appropriate, I/Ointerface 1508 may include one or more device or software driversenabling processor 1502 to drive one or more of these I/O devices. I/Ointerface 1508 may include one or more I/O interfaces 1508, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1510 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1500 and one or more other computer systems 1500 or oneor more networks. As an example and not by way of limitation,communication interface 1510 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1510 for it. As an example and not by way oflimitation, computer system 1500 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1500 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1500 may include any suitable communicationinterface 1510 for any of these networks, where appropriate.Communication interface 1510 may include one or more communicationinterfaces 1510, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1512 includes hardware, software, or bothcoupling components of computer system 1500 to each other. As an exampleand not by way of limitation, bus 1512 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1512may include one or more buses 1512, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by a first client systemassociated with a first user: initiating, via a communication network, acommunication session with a second client system associated with asecond user, wherein the communication session is initiated in a firstmodality; receiving, from the communication network, a ping to the firstclient system to evaluate available bandwidth on the communicationnetwork; estimating, by the first client system, an amount of bandwidthavailable on the communication network for use by the first clientsystem; determining, by the first client system, the amount of bandwidthavailable on the communication network for use by the first clientsystem is insufficient for the first modality; and switching, by thefirst client system, the communication session with the second clientsystem to a second modality, wherein the second modality uses lessbandwidth than the first modality.
 2. The method of claim 1, whereinestimating the amount of bandwidth available on the communicationnetwork for use by the first client system is based on antenna strengthassociated with the first client system.
 3. The method of claim 1,wherein the second client system converts the communication session tothe first modality.
 4. The method of claim 1, wherein the first modalitycomprises video, audio, or text.
 5. The method of claim 1, wherein thecommunication session comprises a video clip, an audio clip, or a textstring.
 6. The method of claim 1, wherein the first modality is video,and wherein the method further comprises: determining the amount ofbandwidth available on the communication network for use by the firstclient system is sufficient for the communication session to be inaudio.
 7. The method of claim 6, wherein the second modality is audio.8. The method of claim 1, wherein the first modality is video, andwherein the method further comprises: determining the amount ofbandwidth available on the communication network for use by the firstclient system is insufficient for the communication session to be inaudio.
 9. The method of claim 8, wherein the second modality is text.10. The method of claim 1, wherein the first modality is audio and thesecond modality is text, and wherein switching the communication sessionwith the second client system to the second modality is based onautomated speech recognition by an audio speech recognition (ASR)module.
 11. The method of claim 10, further comprising: identifying oneor more sounds associated with the audio; and adding one or moredescriptions of each of the one or more sounds to the text.
 12. Themethod of claim 1, wherein the first modality is video and the secondmodality is audio, and wherein switching the communication session withthe second client system to the second modality comprises: identifyingone or more vocalizations associated with the video; and generating theaudio based on the one or more vocalizations.
 13. The method of claim12, further comprising: identifying one or more gestures associated withthe video; generating an audio representation of the one or moregestures; and adding the audio representation to the generated audio.14. The method of claim 1, wherein the first modality is video and thesecond modality is text, and wherein switching the communication sessionwith the second client system to the second modality comprises:identifying one or more vocalizations associated with the video; andgenerating the text based on the one or more vocalizations by an audiospeech recognition (ASR) module.
 15. The method of claim 14, furthercomprising: identifying one or more gestures associated with the video;generating one or more descriptions for each of the one or moregestures; and adding the generated descriptions to the text.
 16. Themethod of claim 1, wherein the communication session is associated witha network interface that communicates with the communication network,and wherein the amount of bandwidth available is estimated based on oneor more attributes of the network interface.
 17. The method of claim 1,wherein estimating the amount of bandwidth available comprises:receiving, at the first client system, a predetermined quantity of datavia the communication network; and measuring a rate at which thepredetermined quantity of data is received.
 18. One or morecomputer-readable non-transitory storage media embodying software thatis operable when executed to: initiate, via a communication network, acommunication session with a second client system associated with asecond user, wherein the communication session is initiated in a firstmodality; receive, from the communication network, a ping to the firstclient system to evaluate available bandwidth on the communicationnetwork; estimate, by the first client system, an amount of bandwidthavailable on the communication network for use by the first clientsystem; determine by the first client system, the amount of bandwidthavailable on the communication network for use by the first clientsystem is insufficient for the first modality; and switch, by the firstclient system, the communication session with the second client systemto a second modality, wherein the second modality uses less bandwidththan the first modality.
 19. A system comprising: one or moreprocessors; and a non-transitory memory coupled to the processorscomprising instructions executable by the processors, the processorsoperable when executing the instructions to: initiate, via acommunication network, a communication session with a second clientsystem associated with a second user, wherein the communication sessionis initiated in a first modality; receive, from the communicationnetwork, a ping to the first client system to evaluate availablebandwidth on the communication network; estimate, by the first clientsystem, an amount of bandwidth available on the communication networkfor use by the first client system; determine by the first clientsystem, the amount of bandwidth available on the communication networkfor use by the first client system is insufficient for the firstmodality; and switch, by the first client system, the communicationsession with the second client system to a second modality, wherein thesecond modality uses less bandwidth than the first modality.